CHAOTIC ANALOG-TO-INFORMATION CONVERSION: PRINCIPLE AND RECONSTRUCTABILITY WITH PARAMETER IDENTIFIABILITY
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Publication:5407274
DOI10.1142/S0218127413501988zbMath1284.94022arXiv1212.2725MaRDI QIDQ5407274
Feng Xi, Shengyao Chen, Zhong Liu
Publication date: 7 April 2014
Published in: International Journal of Bifurcation and Chaos (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/1212.2725
Signal theory (characterization, reconstruction, filtering, etc.) (94A12) Complex behavior and chaotic systems of ordinary differential equations (34C28)
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Cites Work
- Enhancing sparsity by reweighted \(\ell _{1}\) minimization
- CoSaMP: Iterative signal recovery from incomplete and inaccurate samples
- Numerical parameter identifiability and estimability: Integrating identifiability, estimability, and optimal sampling design
- Templates for convex cone problems with applications to sparse signal recovery
- A COMPRESSED SENSING FRAMEWORK OF FREQUENCY-SPARSE SIGNALS THROUGH CHAOTIC SYSTEM
- On sparse reconstruction from Fourier and Gaussian measurements
- Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information
- Decoding by Linear Programming
- Near-Optimal Signal Recovery From Random Projections: Universal Encoding Strategies?
- Signal Recovery From Random Measurements Via Orthogonal Matching Pursuit
- Sharp Thresholds for High-Dimensional and Noisy Sparsity Recovery Using $\ell _{1}$-Constrained Quadratic Programming (Lasso)
- Beyond Nyquist: Efficient Sampling of Sparse Bandlimited Signals
- Bregman Iterative Algorithms for $\ell_1$-Minimization with Applications to Compressed Sensing
- Optimally sparse representation in general (nonorthogonal) dictionaries via ℓ 1 minimization
- Compressed sensing